Name
Title | ||
|---|---|---|
A new CBIR system using sift combined with neural network and graph-based segmentation |
Abstract | ||
|---|---|---|
In this paper, we introduce a new content-based image retrieval (CBIR) system using SIFT combined with neural network and Graph-based segmentation technique. Like most CBIR systems, our system performs three main tasks: extracting image features, training data and retrieving images. In the task of image features extracting, we used our new mean SIFT features after segmenting image into objects using a graph-based method. We trained our data using neural network technique. Before the training step, we clustered our data using both supervised and unsupervised methods. Finally, we used individual object-based and multi object-based methods to retrieve images. In the experiments, we have tested our system to a database of 4848 images of 5 different categories with 400 other images as test queries. In addition, we compared our system to LIRE demo application using the same test set. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12145-6_30 | ACIIDS (1) |
Keywords | Field | DocType |
new mean sift feature,training data,retrieving image,graph-based segmentation,neural network,new cbir system,new content-based image retrieval,image feature,graph-based segmentation technique,neural network technique,multi object-based method,cbir system,system performance,image features | Scale-invariant feature transform,Computer science,Image retrieval,Artificial intelligence,Artificial neural network,Training set,Graph,Computer vision,Pattern recognition,Feature (computer vision),Segmentation,Machine learning,Test set | Conference |
Volume | ISSN | ISBN |
5990 | 0302-9743 | 3-642-12144-6 |
Citations | PageRank | References |
5 | 0.44 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nguyen Duc Anh | 1 | 58 | 4.33 |
| Pham The Bao | 2 | 22 | 7.70 |
| Bui Ngoc Nam | 3 | 5 | 0.44 |
| Nguyen Huy Hoang | 4 | 8 | 2.92 |